The managed platform for AI agents

Your first agent, working in two weeks

Don't start from scratch. The platform already exists.

Integrations
Memory
Scheduled routines
Evaluations
No per-token fees
Observability
Agent sandbox
Self-learning

Build the agent that differentiates your product, not the platform under it

Integrations

Plug into any platform your product or workflow already uses, each connection scoped to an identity, an authority, and a lifecycle.

Multi-dimensional memory

The agent remembers what it should, and only what it should.

Separate memory for each person, conversation, and customer
Nothing learned in one place resurfaces somewhere it does not belong
Answers cite their source and check that it is current

Multi-surface

The agent reaches people where the work already happens — Slack, email, SMS and RCS, or inside your own product.

Scheduled routines

Recurring and event-driven work that pushes to you, so nobody has to remember to ask.

Evaluations gate every release

Any change to a model, prompt, tool, or budget becomes a candidate that has to earn its promotion.

Observability you own

Every run is traced, and the traces are yours to keep rather than ours to hold.

Exports to any OpenTelemetry backend
Traces carry no message content unless you ask for it
Full transcripts when you need the exchange itself

Agent sandbox, out of the box

Fresh sandbox per program, destroyed on exit. When a connection credential enters it, that credential — not a network filter — is the boundary. Every Connection is bound to an identity and carries only the read and write authority it was granted.

Isolation is logical on shared infrastructure, and Cantora publishes that limit, and every other one, beside the control it qualifies.

Self-learning

Cantora notices when repeated work has become stable and offers to remember it. It pairs the program you approved with a note about when to reuse it, and never changes itself or schedules improvements on its own.

See the difference, then run your own numbers

$168,200Estimated savings at the pricing calculator's default inputs

4 monthsSooner at those same inputs

See the full pricing

You own the workflow and all the data, Cantora runs the platform underneath

A managed platform — not a framework, not a library.

You own	Cantora runs
The workflow and what the agent should do	The runtime that executes it
Business context and source data	Integrations, context assembly, and grounded retrieval
The acceptance boundary and the definition of success	Evaluations, release gating, canary, and rollback
The business result	Everything under it — quality, latency, reliability, and cost

How Cantora runs it

Code is the primary tool for structured-data work

How did this week compare with last week?

Revenue is up +$17,653.55

the program Cantora wrote1,023 records read

// Runs once, in a fresh sandbox, under a read credential
// scoped to this customer by the provider itself.
const orders = await commerce.orders.list({
  placedAfter: "2026-07-14",
  placedBefore: "2026-07-28",
});

return Object.entries(Object.groupBy(orders, byIsoWeek))
  .map(([week, weekOrders]) => ({
    week,
    orders: weekOrders.length,
    revenue: sum(weekOrders.map((o) => o.total)),
  }));

Ran once in a fresh sandboxTenant-scoped read credential, finite time and resources, destroyed after the call.

what the model readthe whole answer

[
  { "week": "2026-W29", "orders": 512, "revenue": 184230.55 },
  { "week": "2026-W30", "orders": 511, "revenue": 201884.10 }
]

95.2%token reductionThe median across six measured structured-data workloads, ranging from 75.3% to 99.3%.

Ask a question to an agent about a thousand records, and more platforms make the model read all thousand. Cantora writes a program instead: the model reads the result, not the records.

See the full code operation

Fewer tokens is the smallest part of it

Correctness

Math runs in code, so a sum is a sum and does not drift between runs.

Better answers

A short result leaves the model room to reason about what actually matters.

Speed

Less for the model to read and write, so the answer arrives sooner.

Evidence

The exact program and its result are kept, so you can see what actually ran instead of taking the agent's word for it.

Get started

Come with the vision. We’ll do the rest.

Your first agent, working in two weeks.

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